Patterns of variability of sea surface chlorophyll in the Mozambique Channel:
A quantitative approach
Emilie Tew-Kai, Francis Marsac
PII:
DOI:
Reference:
S0924-7963(08)00315-1
doi: 10.1016/j.jmarsys.2008.11.007
MARSYS 1745
To appear in:
Journal of Marine Systems
Received date:
Revised date:
Accepted date:
18 June 2008
4 November 2008
11 November 2008
Please cite this article as: Tew-Kai, Emilie, Marsac, Francis, Patterns of variability of
sea surface chlorophyll in the Mozambique Channel: A quantitative approach, Journal of
Marine Systems (2008), doi: 10.1016/j.jmarsys.2008.11.007
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Patterns of variability of sea surface chlorophyll in the Mozambique
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Channel: a quantitative approach
UR 109 THETIS, Centre de Recherche Halieutique, Avenue Jean Monnet - BP 171,
Sète Cedex
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Emilie Tew-Kai1 and Francis Marsac1
* Email : emilie.tewkai@ird.fr
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Abstract
We analyse the coupling between sea surface chlorophyll concentration (CC) and
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the physical environment in the Mozambique Channel (MZC) using statistical models.
Seasonal and interannual patterns are studied along with the role of mesoscale dynamics on
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enhancement and concentration processes for phytoplankton. We use SeaWifs data for CC
and two other remotely sensed data sets, TMMI for sea surface temperature (SST) and
merged altimetry products for sea level anomaly and geostrophic current. Empirical
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Orthogonal Functions (EOF) on SSC and SST show strong seasonality and partition the
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MZC into three distinct sub-areas. The chlorophyll variability is mostly driven by
seasonality, but more in the North (10°S-16°S) and South (24°S-30°S), and explains
respectively 64% and 82% of the CC variance. In the Central part (16°S-24°S), the
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seasonal signal has less influence (60% variance). There, complex EOFs on Sea Level
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Anomaly (SLA) highlight the role of mesoscale activity (i.e. eddies and filament structures)
in the spatial distribution of chlorophyll. Five mesoscale descriptors (shear, stretch,
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vorticity, deformation and eddy kinetic energy) are derived from the altimetry data to
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quantify the eddies-related physical patterns in the central region of the MZC. We use
generalized Additive Models to explain the effect of those features on phytoplankton
enhancement. The best model fit (r²=0.73) includes shear, stretch, vorticity and the latitudelongitude interaction as eddies are well structured in space. Cyclonic eddies associated with
negative vorticity are conductive to phytoplankton enhancement by the effect of upwelling
in the core notably during the spin-up phase. The interaction between eddies generate
strong frontal mixing favourable to the production and aggregation of organic matter. The
mesoscale activity is also affected by interannual variability with consequences on CC. We
highlight a substantial reduction of the SLA pattern in 2000-2001 when the SOI positive
phase is peaking (Nina-type pattern). The strong relationship between mesoscale eddies
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and SOI suggests that primary productivity in the MZC is also under the influence of
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distant forcing at a basin scale.
Key words: Mozambique Channel, Sea Surface chlorophyll, mesoscale, seasonal
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variability, climate forcing, quantitative approach.
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1. Introduction
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The pelagic ecosystem, as reflected by the distribution of the living organisms,
is heterogeneous at various space- and time-scales (Fennel 2001, Hyrenbach et al,
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2007; Cotté et al, 2007; Gaspar et al, 2006). At the first levels of the food web,
biological patterns are strongly coupled to physical processes (Denman et al., 1977;
Steele, 1989). Since the early 80s, the satellite imagery has been intensively used to
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characterize spatial patterns in the global ocean. After 1997, as the chlorophyll content
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at the sea surface became measurable routinely from the space (SeaWiFS), direct
studies of the linkages between climate and biology (Yoder et al 2003, Uz and Yoder
2004, Murtugudde et al 1999, 2004) and their consequences on marine ecosystem
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dynamics (Brentnall et al 2003) could be developed.
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Phytoplankton concentration is bound to seasonal variation in temperate areas
(Levy et al, 1999) and to a lesser extent in the tropical areas. But the occurrence of
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plankton patches and local blooms can occur independently of the season at mesoscale.
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Martin et al (2003) highlighted the complexity of interactions between endogenous
(physiology) and external (environment) factors to maintain the local algal production.
The KISS theory developed by Okubo stipulates that two antagonist processes
influence the development of phytoplankton patches. Physiological processes lead the
growth of algal populations, then dispersion processes decrease phytoplankton biomass
from the patches. Features such as fronts, upwelling, river plumes and eddies play a key
role in the dynamics and functioning of regional ecosystems (Falkowski et al 1991,
Strass 1992, Flierl and Davis 1993, Dadou et al 1996, Spall and Richards 2000, Martin
et al 2001). The influence of mesoscale eddies on nutrient supply in the euphotic zone,
new production (McGillicuddy and Robinson 1997; McGillicuddy et al 1998) and
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development of zooplankton communities (Huskin et al 2001, Lee and Park 2002, Kang
et al 2004) has been well studied. Lima et al (2002) underline the role of the turbulent
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eddy field, eddies’ edge and eddy-eddy interaction in boosting the primary production.
In the Indian Ocean, the Mozambique Channel (MZC) is a region where the
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mesoscale activity is strongly developed. It can even be considered as a natural
laboratory to analyse the coupling between eddies and primary production. Cyclonic
and anticyclonic eddies are generated in the narrower part of the MZC, at 17°S, and
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propagate southwards along the coast of Mozambique (Schouten et al. 2003; Quartly
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and Srokosz, 2004, de Ruijter et al., 2002). The MZC consists of three linked systems.
In the North, the Rossby forcing dominates and generates eddies at a frequency of
seven per year. Then in the Central zone of the MZC, eddies become more energetic
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and the frequency slows down to 5 per year. Finally in the South, eddies merge with
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those travelling from the southern tip of Madagascar and cause a subsequent reduction
in the frequency of the mesoscale signal, at 4 per year, before they get trapped in the
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Agulhas Current. The shape of the MZC and its seabed topography mostly drive these
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peculiarities that are likely to affect the distribution of biological enrichment in
different manners. So far, phytoplankton enrichment processes have been explored in
the region mainly with descriptive approaches (Quartly and Srokosz 2004, Zubkov et
al. 2003).
The aim of this paper is to develop a quantitative analysis of the coupling
between physical features and surface chlorophyll concentration at various scales
(seasonal and interannual) with emphasis on the role of mesoscale eddies as drivers of
the biological enrichment. In a first phase, we shall characterize the spatio-temporal
variability of sea surface chlorophyll content (CC), Sea Surface Temperature (SST) and
Sea Level Anomaly (SLA) in the whole Mozambique Channel, using empirical
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orthogonal functions (EOF). In a second phase, we shall focus on the influence of
2. Data and methods
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2.1. Chlorophyll concentration from ocean colour data
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mesoscale eddies on CC, using generalized additive models.
The SeaWiFS global area coverage can be obtained as 8-day composites with a
spatial resolution of 9 km (level 3 product). We extracted original data for the study
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area (10°S-30S / 30°E-50°E) and reflectance values were converted into chlorophyll
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concentration (Chl-a, mg.m-3) by the algorithm developed by O’Reilly et al. (1998).
The study covers a period of 88 months (September 1997 to December 2004)
representing an uninterrupted sequence of 335 weekly images available through the
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Goddard DAAC (http://daac.gsfc.nasa.gov). In the region of interest, the chlorophyll
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concentration (CC) exhibits large differences between coastal regions and the high sea.
CC does not exceed 1 mg.m-3 in the deep sea of the MZC when concentrations in the
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coastal areas can be greater by one order of magnitude. As this study focuses on the
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processes in the deep sea, we have masked the continental shelf areas where the depth
is less than 200 m.
Bio-optical variability of the ocean follows a log normal distribution (Cambell
1995). Chlorophyll is a bio-optical particle that varies spatially by over three orders of
magnitude. Hence, ocean colour data were log-transformed prior to any data treatment.
The EOF method used in this paper requires no missing value in the data set, whereas
pixels can be masked by clouds in visible remotely-sensed data. Spatial gaps were
filled-in with a two-step interpolation procedure (kriging in space, then principal
component analysis in time for each pixel, i.e. Eigenvector Filtering method).
2.2. Sea surface temperature data
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We used the TRMM Microwave Imager (TMMI) data produced by the Remote
Sensing Systems sponsored by the NASA Earth Science REASON DISCOVER
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Project. The data are available weekly on a 0.25° grid. As TMI is a microwave product,
the spatial resolution is much less than visible sensors (such as AVHRR) but in return,
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the clouds have no effect on the measurement.
2.3. Mesoscale descriptors
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Sea level anomaly (SLA) and the zonal (U) and meridional (V) components of the
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geostrophic current are produced by the Ssalto/Duacs multimission project and
distributed by Aviso (http://www.aviso.oceanobs.com/) with support from CNES.
Merged SLA data have a resolution of 0.25 degree and merged U and V have a
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resolution of 0.33 degree. Those products are a composite of the three sensors from
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TOPEX POSEIDON, JASON and ERS-2 satellites. We used weekly time steps for
1998-2004 and we derived five parameters characterizing the dynamical velocity field.
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Vorticity is a reliable descriptor of the rotation of the water mass (Eq.1), shear (Eq.2),
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stretch (Eq.3) and deformation rate (Eq.4) are components of filament quantity (fig.2.)
and EKE (Eddy Kinetic Energy) (Eq.5) is an indicator of the intensity of the movement
of the water mass:
Vorticity: Z
Stretch:ın: V n
Shear ıs: V s
wv wu
(Eq.1),
wx wy
wu wv
(Eq.2),
wx wy
wv wu
(Eq.3),
wx wy
Deformation=ın2+ıs2 (Eq.4),
Eddy Kinetic Energy: EKE
(U ² V ²)
(Eq.5).
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2.4. Patterns detection
The EOF analysis is used to summarize the dominant space and time patterns
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from the series of satellite images (Nezlin and McWilliams, 2003). The multivariate
gridded data used consist of time series (t) over a spatial grid (latitude l and longitude
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L). The field F (l x L, t) is such than each rows corresponds to a time series and each
column to one map. In order to save computational time and achieve a more stable and
robust decomposition, we used the Singular Value Decomposition method (Venegas,
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2001) instead of the classical covariance matrix approach. F (n, m) is composed of
location and m, the time coordinate.
U .S .V T
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F
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centred data (overall mean subtracted from observations) with n representing spatial
U (n, m) is the left singular vectors corresponding to the eigenvectors in a classical
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EOF (spatial mode) and V is the right singular vector, with
S * V T , where A (n, n) is the temporal amplitude in a classical EOF.
S k , where O k is the kth eigenvalue in a classical EOF.
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that: O k
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The singular value S is proportional to the eigenvalue obtained by a classical EOF such
We also used Complex EOF (or CSVD) to allow an efficient detection of
propagating features, such as mesoscale eddies. This method also completes the initial
SVD approach as it emphasizes signature of low amplitude modes (see Susanto et al.,
1998 for a full description). In terms of computational procedure, the difference lies in
the initial matrix F (n, m) which is transformed into a complex signal using the Hilbert
transformation. Then U, V and S are determined as in the equation (2). The moving
features of the original field are described by four parameters in the CSDV: the spatial
amplitude, the spatial phase, the temporal amplitude and the phase. In this paper, we
shall only represent the spatial and temporal amplitudes. The combination between the
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spatial and temporal amplitudes, i.e. variability of a phenomenon is obtained by
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multiplying the spatial and temporal modes (Baldacci et al., 2001).
2.5. Regression models
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The exploration of functional linkages between sea surface chlorophyll and
physical fields was undertaken with Generalized Additive Models (GAMs)
(McCullagh and Nelder, 1989; Chambers and Hastie, 1992, Hastie & Tibshirani, 1990).
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GAMs are nonparametric generalizations of multiple linear regression techniques. This
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method allows in particular not to be in a rigid parametric environment, expressing the
dependence enter the variable to be explained and explanatory variable. The pros of
GAMs are they enable multivariate analysis with no a priori assumptions of linearity.
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Linear and additive predictors are related to the mean of Y by a link function g, which
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is monotone and non differentiable.
The general formulation of the GAM is given by (Eq.6):
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E[Y ]
D ¦ f j (X j )
g (P )
(Eq.6), where Yi is the dependent variable for
j 1
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observation i, Xj, are covariates and fj correspond to the unknown (non-parametric)
functions estimated by smoothed operators. We took the cubic regression spline with
shrinkage as smoothed method. The criterion used to select the most appropriate model
is the generalized cross validation (GCV), analogous to the Akaike information
criterion in GLM), which is defined as:
GCV
nD
, where n is the number of samples, D the deviance and df the
(n df ) 2
effective degrees of freedom of the model.
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3. Patterns of variability of temperature, chlorophyll and altimetry field
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The pattern revealed by the first EOF mode of the sea surface temperature (SST)
field holds 89.6% of the variance. The signal is essentially seasonal as reflected by the
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time series in Fig 3b. There are clear differences from north to south of the MZC,
although the SST varies in phase in the whole region as all spatial loadings have the
same sign (Fig. 3a). North of 17°S (red area), the seasonal amplitude is around 4°C
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whereas a greater magnitude (5.7°C) characterizes the south west of Madagascar
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(purple area). Overall, the seasonal amplitude gradually increases from North to South
of the channel. South east of Madagascar, a core area with relatively weaker seasonal
amplitude is found in the lee of the southern limb of the East Madagascar current
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flowing poleward along the eastern shelf of Madagascar. The second EOF mode (not
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shown) holds 1.9% of the variance; it mostly reflects the interannual variability that is
very minor here compared to the seasonal variability.
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The first EOF of the sea surface chlorophyll field explains 31.9% of the
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variance. The time sequence is driven by the seasons with a larger inter-annual
variability than for SST (Fig. 4b). The highest levels of CC occurred during the
southwest monsoons of 2002 and 2003. Except for areas stretching along the western
shelf of Madagascar, all spatial loadings of CC are positive and indicate a phased
seasonal variability in all locations (Fig 4a). The CC peaks in August-September that is
the austral winter. Unlike the SST field showing a North-South gradient, CC exhibits an
East-West gradient north of the latitude 24°S, with increasing CC from Madagascar to
the African mainland. South of 24°S, the enrichment in phytoplankton stretches out
from the South of Madagascar, where the signature of the coastal upwelling is very
clear. Actually, the coastal part of the upwelling is not shown because we applied a
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mask on the shelf areas (delineated by the isobaths 200 m) to exclude high and local
values from the regional analysis.
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The SLA field in the MZC points out very well-marked mesoscale features in
the central and southern portions of the channel. SLA was processed with complex
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EOFs (CEOF) and the first mode holds 10.4% of the variance. This mode is similar to
the second mode of the classical EOF that contains only 5.4% of the variance (not
shown). Compared to a classical EOF, the use of the Hilbert transformation gives a
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much better precision in delineating these moving features on a statistical basis. The
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spatial pattern is particularly clear in the central part of the MZC (Fig. 5a). It displays a
corridor of 3 core eddies of opposite sign from one to another, 100 x 200 km in size, in
a south-westerly direction. In the southern portion of the channel, alternating eddies of
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smaller magnitude can be discernable across the region, from the Southern tip of
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Madagascar to the African coast along 28°S. The time component of the CEOF 1
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2001 (Fig. 5b).
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highlights a substantial reduction of the mesoscale activity in 2000 and first semester of
4. Physical forcing on chlorophyll pattern
The East-west gradient in CC mentioned earlier suggests that other drivers than
the sole seasonal cycle, are involved in the spatial distribution of CC. We explored the
amount of variance contained in the CC field. Three distinct areas were examined on
the basis of information revealed by the spatial pattern of the first EOFs: North between
latitudes 10°S-16°S, Central for 16°S-24°S and South for 24°S-30°S. We decomposed
the CC field into monthly time series and seasonal time series and quantified the
variability due to the seasonal signal in the whole channel and in each of the three
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areas. Overall, the CC variability in the MZC is driven by a clear seasonal signal
(68.9% of the total variance). But the Central area appears less related to the seasonal
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cycle (60.1% of the total variance) than are the North and South areas (64 and 82% of
the total variance explained by seasonality respectively). Indeed, eddies are well-
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developed in the central area, and this simple test confirms that propagating eddies
introduce a prominent non-seasonal mode into the underlying seasonal processes.
An interannual mode is also evidenced in the CEOF 1 time component of the
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SLA with depressed amplitude in 2000 and 2001 (Figure 5b). Varying amplitudes are
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also noted in the EOF 1 of the CC (Figure 4b). In order to compare the interannual
trends of both factors, we applied a cumulative deviation on the monthly anomalies. For
CC, subtracting the seasonal component from the original series produces anomalies.
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The analysis is restricted to the central area of the MZC (Figure 6a). Both time series
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are highly correlated with reversed patterns (r=-0.87). We find two phases along the 7
years of the study period. During the first phase (1998-2000), eddies are predominantly
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anticyclonic and chlorophyll is below normal. During the second phase (mid-2001-
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2004), cyclonic eddies are dominant and chlorophyll is enhanced. Unlike the SLA
signal with comparable amplitudes in both phases, CC displays much higher amplitudes
during the positive phase. In order to relate this local response to basin-scale forcing,
we cumulated the Southern Oscillation Index (SOI) over time (Figure 6b). We found
that the transition between two phases (mid 2000 to mid-2001) corresponds to the peak
of the positive SOI phase (denoting Niña-type patterns).
5. Coupling mesoscale structures with chlorophyll distribution
5.1. Descriptive approach
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We selected a SeaWiFS scene with minimal cloud cover (26-Feb to 5-March
1998) and the corresponding SLA scene (25-Feb 1998) to illustrate the structuring
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effect of mesoscale features on chlorophyll concentration. The cloud cover (23.6% of
the pixels) is mostly restricted to the northern part of the MZC as shown on Fig. 7a. The
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high chlorophyll concentrations of the shelf areas along Africa and West Madagascar
(> 1 mg.m-3) highlight the sharp contrast with the less productive waters of the deep sea
(average 0.15 mg.m-3). The use of a 200-m depth mask is due to exclude these coastal
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systems from the analysis. Both coastal mask and chlorophyll-interpolated values under
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clouds are shown in Fig 7b. The CC field exhibits well-marked spatial features, with a
succession of high (E3-E5) and low (E1-E2-E4) chlorophyll areas that are located
mostly on the western part of the MZC. The CC and SLA fields (Fig. 7c) show a good
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coherence between low-chlorophyll concentrations and anti-cyclonic eddies on one
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hand, high-chlorophyll concentrations and cyclonic eddies on the other hand. Filament
features are created at the periphery of anti-cyclonic eddies and seaward transport of
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shelf production of the Sofala Bank (19°S-22°S) along coast of Mozambique is also
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suggested. We shall focus the statistical analysis of the spatial coupling in the central
MZC (16°S-24°S / 36°E-44°E) where responses in SLA and CC are well shown.
5.2. Statistical approach
We investigated the relationships between physical descriptors and the response
of chlorophyll concentration with generalized additive models. The work was initially
conducted on the SeaWiFS and altimetry scenes presented in the previous section, and
also on the SST scenes. In order to prepare coherent data tables containing all pixel
values at the same spatial resolution, we had to downscale the initially 9-km grid of CC
and the 25-km grid of SST and SLA to the 33-km grid of the U and V components of
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the geostrophic current. The eleven variables considered in the analysis are listed in
Table 1. Among those, we had to consider latitude and longitude due to the spatially
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structured nature of the coupling.
It was necessary to study the correlations between variables in order to select the
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most appropriate set of independent variables to incorporate in the models. The
correlation matrix (Spearman coefficients) points out high degree of colinearity
between SST and latitude-longitude, SLA, vorticity and Chl concentration (Table 2).
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Model selection was based on the minimisation of the GCV criteria. The selected
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model is detailed in Table 3. All smoothed terms are highly significant (p<0.001). The
interaction between latitude and longitude and the vorticity are the variables that mostly
contribute to the deviance of the model. The selected GAM explains 75.1% of the total
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deviance.
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Graphic representations of the GAM regression are given in Fig. 8.
strongest positive responses of chlorophyll concentration are located along the western
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shelf of the narrow section of the Channel (16°S-17°S) and off Sofala Bank (19°S-
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21°S) (Fig. 8a). The latitude-integrated response (Fig. 8b) shows an overall decreasing
trend from North to South, after a maximum value at 17°S. The longitude-integrated
response (Fig. 8c) is a U-shaped curve, where peak values of chlorophyll concentration
are located at the slope of the two bordering continental shelves. Among the structural
descriptors, the stretch (Fig. 8d) has a monotonous positive effect on CC and the shear
(Fig. 8e) has a similar effect in its range of positive values. The chlorophyll
concentrations decrease almost linearly with vorticity (Fig. 8f) reflecting the enhanced
(depressed) primary productivity in the cyclonic (anti-cyclonic) eddies.
In order to test and strengthen the results produced by this single snapshot, we
computed eight other GAMs shared out among summer (February) and winter
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(August). We only introduced mesoscale descriptors in the model, excluding latitude
and longitude. We selected scenes with minimal cloud cover in order to maximize the
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number of chlorophyll information in the analysis. The same set of mesoscale
descriptors was used to compare the models. Results are presented in Table 4. The
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percent of deviance explained by mesoscale activity is satisfactory, ranging from 13.7
to 43.7% of the total deviance, and no specific trend is noted from summer to winter. In
all models, vorticity is highly significant. On the other hand, descriptors of eddy
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boundary are not significant everywhere but at least one of those is found significant in
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each model. The shape of the relationships (not shown) is similar to those found in the
first example (February 1998). Overall, these results emphasize the major role played
by cyclonic eddies on phytoplankton enhancement. Therefore, any change in the
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balance between cyclonic and anticyclonic eddies in the MZC may result in significant
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impacts on the dynamics of consumers. Moreover, dynamical gradients created at the
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6. Discussion
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eddy boundary have statistically a positive influence on phytoplankton enhancement.
6.1. Influence of seasonality on phytoplankton enhancement
The seasonal cycles of SST and CC in the MZC are well summarized by the
EOF analysis. The seasonal signal dominates in the northern (10°S-16°S) and southern
(24°S-30°S) regions, whereas the variability in the central region (16°S-24°S) is driven
by the mesoscale dynamics. In the northern region of the MZC, the spatial patterns of
SST and CC depict the large anti-cyclonic gyre described by several authors (Piton and
Poulain 1974, Donguy and Piton 1969, 1991, Lujeharms 2004). The northern branch of
the East Madagascar current and the South Equatorial current pass the north tip of
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Madagascar and move onwards to the African coast where the current splits in two
branches. The southern branch then feeds the western limb of the north basin gyre,
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flowing poleward. Particularly noteworthy are the cooler SST and higher CC shown by
the EOF spatial pattern all along the Mozambican coast to 15°S. This feature would
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suggest the existence of an upwelling during the austral summer. At this season, the
mean winds are from a south-easterly direction (Hastenrath and Lamb 1979), thus not
favourable to cause an offshore Ekman transport. Hence, the current along the
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continental slope would drive the presumed upwelling. The SST in the southern and
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eastern limbs of the gyre is rather homogeneous whereas the CC is more spatiallystructured. Indeed, there is no any enrichment process occurring in the course of the
current; hence, the phytoplankton crop vanishes gradually from west to east, which we
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interpret as the effect of grazing by the consumers. Another noteworthy feature is the
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dramatic increase of chlorophyll concentration along the west shelf of Madagascar
during the austral summer. This enhancement is not due to an upwelling (winds are
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from the north-west and the SST is high) but to river runoff. Indeed in the north
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Madagascar, the rivers are often flooded by high precipitations in relation with the
intertropical convergence zone and passing of cyclones during the warm season (Nassor
and Jury, 1998). The spatial pattern of the SLA does not highlight any striking
mesoscale activity in the north MZC.
South of 24°S, the SST exhibits larger seasonal amplitude than in the northern
region of the MZC. The chlorophyll is also found in higher concentration. The primary
productivity is enhanced by three main processes: wind-induced turbulence over most
of the area during the austral winter, upwelling south-east of Madagascar off Fort
Dauphin (Lutjeharms and Machu, 2000; DiMarco et al., 2000, Machu et al 2002), and
presumably, a cyclonic lee-eddy located in the Delagoa Bight off Maputo (Lutjeharms
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2006). The south-east Madagascar upwelling is mostly located on the shelf, thus it
cannot be seen on the SST spatial pattern because of the relatively low spatial
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resolution of the data in comparison to the width of the shelf (<40 km). However, the
CC enrichment caused by the upwelling is discernable in the CC spatial pattern at the
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southern bound of the shelf, where the coastal waters are advected. The core of
relatively warmer waters (appearing in red in Fig. 3a), southeast of Madagascar, is
located in the lee of the southern branch of the East Madagascar current. This poleward
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flowing current can be identifiable in the SST spatial pattern (yellow corridor along the
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eastern shelf of Madagascar). The origin of the SST core is uncertain but we suggest
that it might be linked to the retroflection of the East Madagascar current (Lutjeharms
1988, De Ruijter et al. 2004). Along the western shelf of the South MZC, the Delagoa
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Bight is a large offset of the coastal shelf at 26°S. The southward passing eddies cause
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here a cyclonic cell and presumably, nutrient inflow in the surface waters enhancing the
primary productivity. Such enhancement has been shown only intermittently (Quartly
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and Srokosz 2003). The CC spatial pattern that we present brings evidence of seasonal
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recurrent chlorophyll enrichment in this region. South of Delagoa Bight, a tongue of
cool water along the shelf during the austral winter is seen in the SST spatial pattern
with elevated chlorophyll concentrations associated to this cooling. The cold waters
originate from the upwelling formed in the Natal Bight, between 29°S-30°S, as
evidenced by Lutjeharms et al (1988, 2000) and Meyer et al (2002). The SLA spatial
pattern in the southern MZC highlights a pathway of alternating cyclonic and anticyclonic eddies along 28°S. Those were described as paired vortices along 28°S formed
at the south Madagascar retroflection and travelling westward to ultimately feed the
Agulhas Current (de Ruijter et al 2003).
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In the central region of the MZC the longitudinal gradients in SST and CC are caused
by the passing of eddies in the western region. The SST spatial pattern reflects
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advection of north Mozambique surface water along the Mozambique shelf as the
resultant water transport is to the South (de Ruijter et al 2002). CC is higher in the
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western “corridor” than in the eastern part of the channel.
6.2. Influence of mesoscale eddies on phytoplankton enhancement
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The GAM carried out in the central part of the channel for the week of February
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26, 1998 demonstrated that physical quantities characterizing the core and boundary of
mesoscale eddies explain the spatial distribution of phytoplankton in a significant way.
Cyclonic eddies associated with negative vorticity are conductive to phytoplankton
ED
enhancement, by the effect of divergence at the surface and upwelling in the core of the
PT
eddy. Indeed, such a result was expected. But we demonstrate the role of dynamical
gradients –strong positive shear and stretch- in the concentration process (elevated CC).
CE
Vortices alone cannot generate phytoplankton patches because of dispersing processes
AC
occurring in the core. The formation of persistent phytoplankton patches is the results
of combined effects of vortex dynamics and filament structures generated at meso- and
sub-mesoscale (Levy et al 2001)
Spatial variables have also a clear impact on chlorophyll distribution as depicted
by the interaction between latitude and longitude. Coastal and shelf areas are known to
be more productive than offshore deep water because of land-originated nutrient inputs
and tidal mixing. Although large shelf areas are present in the Eastern side of the MZC
(Madagascar), the primary production is substantially higher on the Western side.
Waters along the African landmass are enriched by two major sources: run-offs of the
4th largest river in Africa, the Zambezi, and mesoscale eddies passing in the west part of
18
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the channel. Chlorophyll-enriched waters from the shelves are advected offshore by the
“succion” effect of those eddies. These observations, and notably the distance to the
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coast, are reflected by the longitude in the GAM analysis. Similarly, there is a
chlorophyll response with latitude: CC declines in a southerly direction, along the
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trajectory of the vortices. We therefore attempt to develop a sketch of the processes
controlling phytoplankton patchiness in relation with mesoscale eddies in the MZC
(Fig. 9).
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The central portion of the MZC (16°S-24°S) can be subdivided in three sub-
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systems. The first one occupies the northern part, more specifically the narrows of the
channel. There, at the narrows of the Channel, eddies are at early life stages. The spinup process of cyclonic eddies is associated with nutrient inflow in the core in the
ED
vertical plane (McGillicuddy and Robinson, 1997; McGillicuddy et al, 1998) that
PT
boosts the primary production. The phytoplankton growth takes place locally: core
water is being isolated from the surroundings and vertical mixing is reduced (Fennel,
CE
2001; Onken, 1990). The second sub-system stretches out in the median part: it is
AC
characterized by eddies becoming mature as they move along the west coast. There,
eddies may control the biological processes. The interaction between anticyclonic and
cyclonic eddies generates high dynamical and complex barriers consisting of multiple
fronts at different scales favourable to phytoplankton enhancement. Such features are
known to play a major role in the production and distribution of chlorophyll surface
locally (Levy et al, 2001; Martin et al, 2002; Lima et al, 2002) by allowing the
exchange of organic matter between the core of the vortex and the periphery.
Furthermore, theoretical studies show that the presence of a vortex in a turbulent field
allows heterogeneous input of resources, and the coexistence of competitive species of
phytoplankton (Bracco et al, 2000;Martin, 2003 - for example). These phenomena
19
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trigger the maturation of the biological compartments from the coast to the deep sea at
the edge of eddies. In some cases, the enrichment process propagates along the food
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chain and lead to favourable foraging areas for top predators, as demonstrated for great
frigatebirds, boobies and sooty terns (Weimerkirch et al, 2004, 2005; Jaquemet et al
SC
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2005). The third sub-system is located in the South part. There, eddies enter a spin-off
process, with decreasing energy and phytoplankton growth is much reduced.
MA
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6.3. Interannual variability of mesoscale activity and surface chlorophyll concentration
Cumulative deviation has pointed out an alternation between anticyclonic (positive) and
cyclonic (negative) phases from 1998 to 2004. A below normal phytoplankton
ED
production is associated with the positive SLA phase (1998-2000) whilst production is
PT
enhanced during the negative phase (2002-2004). A transition occurs from mid-2000 to
mid-2001. Complex EOF computed on the SLA data points out interannual variability
CE
of the mesoscale activity in the MZC, with minimal activity recorded in 2000 and 2001.
AC
The influence of the ENSO/Dipole mode (IOD, Saji et al 1999, Webster et al 1999)
might be suggested to explain this reduced mesoscale intensity around Madagascar.
Palastanga et al (2006) have shown a dramatic reduction of the flow at the narrows of
the MZC in 1997 and 2000-2001. They relate the less intense flow observed in 1997 to
a negative IOD event, but they do not elaborate on the 2000-2001 event that was not
characterized by a prominent negative IOD. Rather, we observe a shift from a moderate
positive IOD (February-2000 to March-2001) to a moderate negative IOD (May to
October 2001). However, the oscillation of the flow shown by Palastanga et al (2006)
corresponds with the oscillation of the cumulated SOI, with lower mesoscale activity
(i.e. weaker southward flow at the narrows of the Channel) during the SOI positive
20
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peak, as shown in this study. These observations are a confirmation that climate forcing
is an important driver of the fluctuations in the shedding rate and intensity of eddies in
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the central MZC.
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Conclusions
Patterns of variability of Sea Surface Chlorophyll in the Mozambique Channel have been
assessed. The influence of ocean processes on the phytoplankton production at different
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spatial and temporal scales has been quantified. The Channel can be decomposed in three sub
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areas. In the north and south seasonality is the dominant physical processes controlling CC.
The central part differs from the two others by the passing of mesoscale eddies. In this place
mesoscale activity seems to be a mainly factor of CC variability, and especially cyclonic
ED
vortex and edge of eddies. Cyclonic vortex allows the upwelling of nutrient and the
PT
enhancement of phytoplankton in the core. Interactions between eddies generate strong
dynamical barriers at meso and sub mesoscale favourable to the phytoplankton. Interannual
CE
variability of mesoscale eddies is negatively correlated to the interannual cycle of CC,
AC
confirming the importance of cyclonic activity in the central part of the channel. In addition,
interannual variability of Sea Level anomaly may be related to climate forcing (IOD/SOI).
This implies that the productivity of the MZC regional ecosystem is also under the influence
of remote forcing at a basin scale. This study shows the need to work at meso and particularly
at sub-mesoscale to better understand the interactions between physical and ecological
processes. Finally the better knowing of these processes appears essential to better understand
the consequences of climate change on mesoscale ocean dynamics and first trophic levels.
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Acknowledgements
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The study was funded by the Institut de Recherche pour le Développement (IRD). The
authors wish to thank Hervé Demarcq (IRD-ECO-UP) for the pre-processing of
AC
CE
PT
ED
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SeaWiFS data.
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Legends to Figures
bathymetry.
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Figure 1 - Major circulatory features and bathymetry in the Mozambique Channel with
The main current and the mesoscale features are schematically shown.
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Hatched areas denote upwelling. In the north of the channel, the coastal current shown is
fed by the South Equatorial Current (SEC) and later depicts a large anticyclonic cell in the
Comoro basin. The white area with black points represents the lee eddy off Angoche. In the
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west, along Mozambique coasts, mesoscale eddies (MCE) move in a southwesterly
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direction. In the east coast of Madagascar, the feature shown is the East Madagascar
Current (EMC) and in the south, the south east Madagascar dipolar eddies (SEME) moving
westward and little north ward. The mesoscale eddies from the Mozambique channel and
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the dipolar structures from the south of Madagascar reach the Agulhas Current (AC).
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(adapted from Lutjeharms, 2003 and Schouten et al, 2003).
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Figure 2 - Sketch representing the different mesoscale descriptors used. The Sla and vorticity
AC
are specific to the core of the eddy. The physical quantities shear, stretch, Eddy Kinetic
Energy (EKE), the SST gradient and the deformation characterize the filaments and the edge
of vortices. Light grey represents a cyclonic eddy (clockwise) and a dark grey an anticyclonic
eddy (anti-clockwise) in the southern hemisphere.
Figure 3 - Spatial pattern (a) and temporal amplitudes (b) of the first EOF computed on SST
data. Percentage of variance explained is 89.6%.
Figure 4 - Spatial pattern (a) and temporal amplitudes (b) of the first EOF computed on Sea
Surface Chlorophyll data. Percentage of variance explained is respectively 31.9%.
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Figure 5 - Spatial pattern (a) and temporal amplitudes (b) of the complex-EOF computed on
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Sea Level Anomaly data. Percentage of variance explained is 10.4%.
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Figure 6 – a) Time series of cumulative deviation of Sea Level Anomalies (solid line) and
chlorophyll concentration anomalies (mg.m-3) (dotted line) in the central part of the
Mozambique Channel from January 1998 to December 2004. b) Cumulative deviation of the
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Southern Oscillation Index.
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Figure 7 - a) Original map of chlorophyll concentration (log(Chla) mg.m-3) for the week
2/26/1998 with clouds (in white). b) Map of chlorophyll concentration (log (Chla) mg.m-3)
ED
with estimation of missing values by interpolation, and the 200-m depth mask. c) Map of Sea
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Level Anomaly (cm) for the same date; E indicates Eddy structures (E1,E2,E4 anticyclonic
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eddies and E3,E5 cyclonic eddies).
AC
Figure 8 - Partial residuals of the nonlinear terms estimated by means of generalized additive
models (GAM) with smoothing splines.
The appropriate smoothness for each applicable model term was selected using generalized
cross validation (GCV). a) The map displays to the non linear relation between the longitude
(long) - latitude (lat) interaction and the sea surface chlorophyll. Mean smooth terms are
summarised on (b) the latitude and (c) the longitude. Effect of the stretch (d), Shear (e) and
vorticity (f) on SSC.
Figure 9 - Sketch of the different phases of eddies in the Mozambique Channel and their
influence on the phytoplankton. 1) In the narrow part of the channel, eddies are being spinned
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up, and highly energetic. The cyclonic eddies can then generate primary production in the
centre by upwelling. 2) Vortices moving along the African coast, reaching a mature stage.
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Anticyclonic eddies export offshore matter from the coast. The succession of cyclonic and
anticyclonic eddies generates strong boundary gradients. These eddy-eddy interactions lead to
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concentrate biomass at eddies’ periphery. Physical processes are conducive to the maturation
of the system. 3) Finally, in the south, eddies are being spinned off and join the Agulhas
AC
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ED
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current.
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AC
CE
PT
ED
MA
NU
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Fig 1
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AC
CE
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ED
MA
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Fig 2
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AC
CE
PT
ED
MA
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SC
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Fig 3
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AC
CE
PT
ED
MA
NU
SC
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Fig 4
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AC
CE
PT
ED
MA
NU
SC
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Fig 5
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AC
CE
PT
ED
MA
NU
SC
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Fig 6
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AC
CE
PT
ED
MA
NU
SC
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Fig 7
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AC
CE
PT
ED
MA
NU
SC
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Fig 8
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AC
CE
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ED
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Fig 9
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Table 1. List of the environmental variables used as input for the models.
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ED
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CE
AC
44
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Units
°C
cm
cm2.s-2
s-1
s-1
s-1
s-1
degree
degree
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Symbol
SST
SST gradient
SLA
EKE
def
SS
SN
vort
Lat
long
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Explicative variables
Temperature
Gradient of temperature
Sea Level anomaly
Eddy kinetic Energy
deformation
Shear
Stretch
Vorticity
Latitude
Longitude
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Table 2. Correlation analysis between the 11 explanatory variables and the sea surface
chlorophyll (sw).
1.00
0.30
0.13
0.76
-0.02
0.18
0.14
-0.19
-0.08
0.20
1.00
0.17
0.19
-0.62
0.07
0.22
0.27
-0.58
0.08
1.00
0.05
-0.15
0.17
-0.02
0.10
-0.05
0.06
sst
sla
1.00
0.01
0.06
0.14
-0.02
-0.04
0.02
1.00
-0.05
-0.28
-0.37
0.84
0.14
eke
ss
sn
vort
def
IP
T
gradsst
SC
R
sw
NU
lat
CE
PT
ED
MA
long
1.00
0.41
0.07
0.03
0.61
-0.04
-0.30
0.14
-0.07
0.00
-0.23
AC
long
lat
sw
gradsst
sst
sla
eke
ss
sn
vort
def
45
1.00
-0.04 1.00
0.02 0.00 1.00
-0.07 -0.24 -0.27
0.14 -0.11 0.06
1.00
0.20
1.00
ACCEPTED MANUSCRIPT
Parametric coefficients:
Estimate
-2.09
Approximate significance of smooth terms:
SC
R
t value
-149.00
IP
T
Table 3. Coefficients of the selected GAM. Smooth terms are represented using penalized
regression spline with smoothing parameters selected by GCV (generalized cross
validation).Parametric coefficients represent the linear terms the model, edf are the estimated
degrees of freedom of the smooth terms using cubic regression spline with shrinkage.
Pr(>|t|)
<2e-16
AC
CE
PT
ED
MA
NU
Edf
F
p-value
deviance
13.25
s(ss)
5.10
36.10
1.700E-04
29.03
s(sn)
0.94
4.00
3.230E-04
40.95
s(vort)
8.89
12.50
< 2e-16
48.05
te(lat:long)
4.98
8.70
< 2e-16
R-sq.(adj) = 0.731 Deviance explained = 75.1% deviance null =53.12
GCV score = 0.04216
46
ACCEPTED MANUSCRIPT
FEB2004
14.8
24.3
***
***
**
**
*
AUG2000
7
18.3
**
**
**
**
MA
ED
PT
CE
AC
47
winter
AUG2001
AUG2002
7.7
14.16
14.5
13.9
***
***
*
*
SC
R
summer
FEB2001
FEB2002
23.4
18.15
13.7
43.7
***
**
**
***
*
*
**
**
**
***
NU
% of clouds
% od devience
gradsla
ss
vort
gradsst
sn
def
FEB2000
27.35
31.9
***
***
***
*
**
IP
T
Table 4. Results of 8 GAMs, computed on 8 SeaWIFs scenes in the Central part of the
Mozambique Channel during winter and summer. Least cloudy scenes were selected for this
analysis. p<0.001;**p<0.01;*p<0.05.
AUG2004
6.6
34
***
*
***
***
*